Machine learning and insurance: the emergence of a pricing revolution

Artificial are a London-based company using AI to take insurers on the journey to frictionless, digital pricing.

A combination of cumbersome paper-based processes, a lack of transparency and an undeniably poor customer experience is how most would describe the purchase of commercial insurance products today. The team at Artificial are looking to change that, with their development of Machine Learning pricing being just one example of disruptive technology that is delivering better outcomes for customers.

Matrix pricing and the risk of high exposure

Traditional commercial insurance pricing models are simplistic and make use of basic matrix systems which use a small number of variables to alter the premium.

Matrix pricing models often lead to uncontrollably high exposure to risk and large loss ratios, with no guarantee that a given policy price is indeed linked to the actual risk. The lack of additional data inputs, as well as a strict set of constraints, means that insurers miss out on a level of pricing accuracy that could be achieved with data-driven assistance.

Another cause of high loss ratios is the use of pricing bands. If a pricing band captures a large range of customers, they will all be offered cover at the same price regardless of whether their risk places them at the top or bottom of the risk bracket. Customers which represent a disproportionately high risk can therefore find themselves in a lower pricing band, leaving the insurer with loss ratios that are substantially higher than they should be.

The problem with ML pricing

With the latest developments in artificial intelligence and Machine Learning, insurers can begin to optimise their pricing processes and allow underwriters to concentrate on higher value analysis.

Using these techniques it is also possible to cut out vast swathes of expensive processes whilst still adding value. To streamline the customer experience and avoid repetitive back-and-forth questioning, insurance companies can draw on a wealth of third-party data to supplement their algorithms.

Unfortunately, ML pricing doesn’t come without its challenges – many of which Artificial have outlined in ‘Part One: How can I use AI to improve my company’s pricing?’. For insurance companies that have an existing commercialised product on the market – with its own established pricing structure – ML can create more problems than it solves.

Making the switch to ML blindly can create a ‘black box pricing’ model, where algorithms are trained using vast amounts of data and variables, making the calculations less transparent to the insurer and ultimately less explainable to the customer. For example, a price generated by twenty different risk factors becomes very difficult to explain to a consumer who wants to know why they are suddenly paying more than their neighbour for the same product.

Besides, new pricing engines often conflict with the existing pricing structures insurers already have in place. If a pricing model has been established for a product that’s already on the market, it’s hugely inconvenient – and costly – to throw it out and start again.

Making the most of data at Artificial

At Artificial, the team of data scientists are applying ML techniques to help take insurance companies on a journey to full digital pricing and negotiation. The company has developed systems to implement ML pricing models for both new and existing commercial and consumer products without the chaos of a restructure – showing that, in the right hands, ML can be hugely beneficial to the insurance industry. To develop a new pricing model, Artificial makes use of internal and third-party claims and policy data to train the algorithms.

With access to new streams of data, the team are beginning to generate insights into the causes of claims and unexplored risk factors. Johnny Bridges, Artificial co-founder and CPO, explains their recent analysis of telematics data: